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A Review of Bootstrap Methods in Ranked Set Sampling

A Review of Bootstrap Methods in Ranked Set Sampling

Arpita Chatterjee, Santu Ghosh
Copyright: © 2022 |Pages: 19
ISBN13: 9781799875567|ISBN10: 1799875563|ISBN13 Softcover: 9781799875574|EISBN13: 9781799875581
DOI: 10.4018/978-1-7998-7556-7.ch008
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MLA

Chatterjee, Arpita, and Santu Ghosh. "A Review of Bootstrap Methods in Ranked Set Sampling." Ranked Set Sampling Models and Methods, edited by Carlos N. Bouza-Herrera, IGI Global, 2022, pp. 171-189. https://doi.org/10.4018/978-1-7998-7556-7.ch008

APA

Chatterjee, A. & Ghosh, S. (2022). A Review of Bootstrap Methods in Ranked Set Sampling. In C. Bouza-Herrera (Ed.), Ranked Set Sampling Models and Methods (pp. 171-189). IGI Global. https://doi.org/10.4018/978-1-7998-7556-7.ch008

Chicago

Chatterjee, Arpita, and Santu Ghosh. "A Review of Bootstrap Methods in Ranked Set Sampling." In Ranked Set Sampling Models and Methods, edited by Carlos N. Bouza-Herrera, 171-189. Hershey, PA: IGI Global, 2022. https://doi.org/10.4018/978-1-7998-7556-7.ch008

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Abstract

This chapter provides a brief review of the existing resampling methods for RSS and its implementation to construct a bootstrap confidence interval for the mean parameter. The authors present a brief comparison of these existing methods in terms of their flexibility and consistency. To construct the bootstrap confidence interval, three methods are adopted, namely, bootstrap percentile method, bias-corrected and accelerated method, and method based on monotone transformation along with normal approximation. Usually, for the second method, the accelerated constant is computed by employing the jackknife method. The authors discuss an analytical expression for the accelerated constant, which results in reducing the computational burden of this bias-corrected and accelerated bootstrap method. The usefulness of the proposed methods is further illustrated by analyzing real-life data on shrubs.

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